Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture Recognition Case Study
Marek Grzegorowski, Sebastian Stawicki
DOI: http://dx.doi.org/10.15439/2015F425
Citation: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 397–405 (2015)
Abstract. The article introduces a novel mechanism for automatic extraction of features from streams of numerical data. It was originally designed for the purpose of processing multiple streams of readings generated by sensors in coal mines. The original research was conducted on methane concentration analysis in the DISESOR project. The article demonstrates an application of the elaborated mechanism for the case of tagging short series of readings from sensors that monitor activities and movements of firefighters during the action with labels corresponding to firefighter activities. The purpose of the experiment was to assess how the automatic feature extraction and construction of classifiers (without parameters tuning and without the use of classifier ensembles) can cope with the competition's task in comparison to other participants.